{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-18T09:20:51.851569Z",
     "start_time": "2024-03-18T09:20:50.398958Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:20:54.503082Z",
     "start_time": "2024-03-18T09:20:54.489554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Available: True\n"
     ]
    }
   ],
   "source": [
    "cuda_available = torch.cuda.is_available()\n",
    "print(\"CUDA Available:\", cuda_available)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:20:56.134996Z",
     "start_time": "2024-03-18T09:20:56.118997Z"
    }
   },
   "outputs": [],
   "source": [
    "class PositionWiseFFN(nn.Module):  #@save\n",
    "    \"\"\"The positionwise feed-forward network.\"\"\"\n",
    "    def __init__(self, ffn_num_hiddens, ffn_num_outputs):\n",
    "        super().__init__()\n",
    "        self.dense1 = nn.LazyLinear(ffn_num_hiddens)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dense2 = nn.LazyLinear(ffn_num_outputs)\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.dense2(self.relu(self.dense1(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:20:57.149683Z",
     "start_time": "2024-03-18T09:20:57.089683Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asu\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:181: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n",
      "  warnings.warn('Lazy modules are a new feature under heavy development '\n"
     ]
    },
    {
     "data": {
      "text/plain": "tensor([[ 0.6627, -0.1939, -0.2102,  0.1333,  0.4955, -0.8198, -0.3921,  0.8556],\n        [ 0.6627, -0.1939, -0.2102,  0.1333,  0.4955, -0.8198, -0.3921,  0.8556],\n        [ 0.6627, -0.1939, -0.2102,  0.1333,  0.4955, -0.8198, -0.3921,  0.8556]],\n       grad_fn=<SelectBackward0>)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffn = PositionWiseFFN(4, 8)\n",
    "ffn.eval()\n",
    "ffn(torch.ones((2, 3, 4)))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:20:58.592730Z",
     "start_time": "2024-03-18T09:20:58.570029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer norm: tensor([[-1.0000,  1.0000],\n",
      "        [-1.0000,  1.0000]], grad_fn=<NativeLayerNormBackward0>) \n",
      "batch norm: tensor([[-1.0000, -1.0000],\n",
      "        [ 1.0000,  1.0000]], grad_fn=<NativeBatchNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "ln = nn.LayerNorm(2)\n",
    "bn = nn.LazyBatchNorm1d()\n",
    "X = torch.tensor([[1, 2], [2, 3]], dtype=torch.float32)\n",
    "# Compute mean and variance from X in the training mode\n",
    "print('layer norm:', ln(X), '\\nbatch norm:', bn(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:20:59.550196Z",
     "start_time": "2024-03-18T09:20:59.532194Z"
    }
   },
   "outputs": [],
   "source": [
    "class AddNorm(nn.Module):  #@save\n",
    "    \"\"\"The residual connection followed by layer normalization.\"\"\"\n",
    "    def __init__(self, norm_shape, dropout):\n",
    "        super().__init__()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.ln = nn.LayerNorm(norm_shape)\n",
    "\n",
    "    def forward(self, X, Y):\n",
    "        return self.ln(self.dropout(Y) + X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:00.200984Z",
     "start_time": "2024-03-18T09:21:00.192984Z"
    }
   },
   "outputs": [],
   "source": [
    "add_norm = AddNorm(4, 0.5)\n",
    "shape = (2, 3, 4)\n",
    "d2l.check_shape(add_norm(torch.ones(shape), torch.ones(shape)), shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:00.867973Z",
     "start_time": "2024-03-18T09:21:00.863972Z"
    }
   },
   "outputs": [],
   "source": [
    "# Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:01.426077Z",
     "start_time": "2024-03-18T09:21:01.421076Z"
    }
   },
   "outputs": [],
   "source": [
    "class TransformerEncoderBlock(nn.Module):  #@save\n",
    "    \"\"\"The Transformer encoder block.\"\"\"\n",
    "    def __init__(self, num_hiddens, ffn_num_hiddens, num_heads, dropout,\n",
    "                 use_bias=False):\n",
    "        super().__init__()\n",
    "        self.attention = d2l.MultiHeadAttention(num_hiddens, num_heads,\n",
    "                                                dropout, use_bias)\n",
    "        self.addnorm1 = AddNorm(num_hiddens, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm2 = AddNorm(num_hiddens, dropout)\n",
    "\n",
    "    def forward(self, X, valid_lens):\n",
    "        Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))\n",
    "        return self.addnorm2(Y, self.ffn(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:01.947602Z",
     "start_time": "2024-03-18T09:21:01.924601Z"
    }
   },
   "outputs": [],
   "source": [
    "X = torch.ones((2, 100, 24))\n",
    "valid_lens = torch.tensor([3, 2])\n",
    "encoder_blk = TransformerEncoderBlock(24, 48, 8, 0.5)\n",
    "encoder_blk.eval()\n",
    "d2l.check_shape(encoder_blk(X, valid_lens), X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:02.882887Z",
     "start_time": "2024-03-18T09:21:02.863887Z"
    }
   },
   "outputs": [],
   "source": [
    "class TransformerEncoder(d2l.Encoder):  #@save\n",
    "    \"\"\"The Transformer encoder.\"\"\"\n",
    "    def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens,\n",
    "                 num_heads, num_blks, dropout, use_bias=False):\n",
    "        super().__init__()\n",
    "        self.attention_weights = None\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_blks):\n",
    "            self.blks.add_module(\"block\"+str(i), TransformerEncoderBlock(\n",
    "                num_hiddens, ffn_num_hiddens, num_heads, dropout, use_bias))\n",
    "\n",
    "    def forward(self, X, valid_lens):\n",
    "        # Since positional encoding values are between -1 and 1, the embedding\n",
    "        # values are multiplied by the square root of the embedding dimension\n",
    "        # to rescale before they are summed up\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self.attention_weights = [None] * len(self.blks)\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X = blk(X, valid_lens)\n",
    "            self.attention_weights[\n",
    "                i] = blk.attention.attention.attention_weights\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:03.544845Z",
     "start_time": "2024-03-18T09:21:03.524846Z"
    }
   },
   "outputs": [],
   "source": [
    "encoder = TransformerEncoder(200, 24, 48, 8, 2, 0.5)\n",
    "d2l.check_shape(encoder(torch.ones((2, 100), dtype=torch.long), valid_lens),\n",
    "                (2, 100, 24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:04.005699Z",
     "start_time": "2024-03-18T09:21:03.992700Z"
    }
   },
   "outputs": [],
   "source": [
    "class TransformerDecoderBlock(nn.Module):\n",
    "    # The i-th block in the Transformer decoder\n",
    "    def __init__(self, num_hiddens, ffn_num_hiddens, num_heads, dropout, i):\n",
    "        super().__init__()\n",
    "        self.i = i\n",
    "        self.attention1 = d2l.MultiHeadAttention(num_hiddens, num_heads,\n",
    "                                                 dropout)\n",
    "        self.addnorm1 = AddNorm(num_hiddens, dropout)\n",
    "        self.attention2 = d2l.MultiHeadAttention(num_hiddens, num_heads,\n",
    "                                                 dropout)\n",
    "        self.addnorm2 = AddNorm(num_hiddens, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm3 = AddNorm(num_hiddens, dropout)\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        enc_outputs, enc_valid_lens = state[0], state[1]\n",
    "        # During training, all the tokens of any output sequence are processed\n",
    "        # at the same time, so state[2][self.i] is None as initialized. When\n",
    "        # decoding any output sequence token by token during prediction,\n",
    "        # state[2][self.i] contains representations of the decoded output at\n",
    "        # the i-th block up to the current time step\n",
    "        if state[2][self.i] is None:\n",
    "            key_values = X\n",
    "        else:\n",
    "            key_values = torch.cat((state[2][self.i], X), dim=1)\n",
    "        state[2][self.i] = key_values\n",
    "        if self.training:\n",
    "            batch_size, num_steps, _ = X.shape\n",
    "            # Shape of dec_valid_lens: (batch_size, num_steps), where every\n",
    "            # row is [1, 2, ..., num_steps]\n",
    "            dec_valid_lens = torch.arange(\n",
    "                1, num_steps + 1, device=X.device).repeat(batch_size, 1)\n",
    "        else:\n",
    "            dec_valid_lens = None\n",
    "        # Self-attention\n",
    "        X2 = self.attention1(X, key_values, key_values, dec_valid_lens)\n",
    "        Y = self.addnorm1(X, X2)\n",
    "        # Encoder-decoder attention. Shape of enc_outputs:\n",
    "        # (batch_size, num_steps, num_hiddens)\n",
    "        Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)\n",
    "        Z = self.addnorm2(Y, Y2)\n",
    "        return self.addnorm3(Z, self.ffn(Z)), state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:04.498670Z",
     "start_time": "2024-03-18T09:21:04.478671Z"
    }
   },
   "outputs": [],
   "source": [
    "decoder_blk = TransformerDecoderBlock(24, 48, 8, 0.5, 0)\n",
    "X = torch.ones((2, 100, 24))\n",
    "state = [encoder_blk(X, valid_lens), valid_lens, [None]]\n",
    "d2l.check_shape(decoder_blk(X, state)[0], X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:04.912849Z",
     "start_time": "2024-03-18T09:21:04.906848Z"
    }
   },
   "outputs": [],
   "source": [
    "class TransformerDecoder(d2l.AttentionDecoder):\n",
    "    def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads,\n",
    "                 num_blks, dropout):\n",
    "        super().__init__()\n",
    "        self._attention_weights = None\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.num_blks = num_blks\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_blks):\n",
    "            self.blks.add_module(\"block\"+str(i), TransformerDecoderBlock(\n",
    "                num_hiddens, ffn_num_hiddens, num_heads, dropout, i))\n",
    "        self.dense = nn.LazyLinear(vocab_size)\n",
    "\n",
    "    def init_state(self, enc_outputs, enc_valid_lens):\n",
    "        return [enc_outputs, enc_valid_lens, [None] * self.num_blks]\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self._attention_weights = [[None] * len(self.blks) for _ in range (2)]\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X, state = blk(X, state)\n",
    "            # Decoder self-attention weights\n",
    "            self._attention_weights[0][\n",
    "                i] = blk.attention1.attention.attention_weights\n",
    "            # Encoder-decoder attention weights\n",
    "            self._attention_weights[1][\n",
    "                i] = blk.attention2.attention.attention_weights\n",
    "        return self.dense(X), state\n",
    "\n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        return self._attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = d2l.MTFraEng(batch_size=128)\n",
    "num_hiddens, num_blks, dropout = 256, 2, 0.2\n",
    "ffn_num_hiddens, num_heads = 64, 4\n",
    "encoder = TransformerEncoder(\n",
    "    len(data.src_vocab), num_hiddens, ffn_num_hiddens, num_heads,\n",
    "    num_blks, dropout)\n",
    "decoder = TransformerDecoder(\n",
    "    len(data.tgt_vocab), num_hiddens, ffn_num_hiddens, num_heads,\n",
    "    num_blks, dropout)\n",
    "model = d2l.Seq2Seq(encoder, decoder, tgt_pad=data.tgt_vocab['<pad>'],\n",
    "                    lr=0.001)\n",
    "trainer = d2l.Trainer(max_epochs=30, gradient_clip_val=1, num_gpus=1)\n",
    "trainer.fit(model, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:19.914135Z",
     "start_time": "2024-03-18T09:21:19.907134Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T13:23:35.484176Z",
     "start_time": "2024-03-18T13:23:24.981819Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[20], line 4\u001B[0m\n\u001B[0;32m      1\u001B[0m engs \u001B[38;5;241m=\u001B[39m [\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgo .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mi lost .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhe\u001B[39m\u001B[38;5;130;01m\\'\u001B[39;00m\u001B[38;5;124ms calm .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mi\u001B[39m\u001B[38;5;130;01m\\'\u001B[39;00m\u001B[38;5;124mm home .\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m      2\u001B[0m fras \u001B[38;5;241m=\u001B[39m [\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mva !\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mj\u001B[39m\u001B[38;5;130;01m\\'\u001B[39;00m\u001B[38;5;124mai perdu .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mil est calme .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mje suis chez moi .\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m      3\u001B[0m preds, _ \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mpredict_step(\n\u001B[1;32m----> 4\u001B[0m     \u001B[43mdata\u001B[49m\u001B[38;5;241m.\u001B[39mbuild(engs, fras), d2l\u001B[38;5;241m.\u001B[39mtry_gpu(), data\u001B[38;5;241m.\u001B[39mnum_steps)\n\u001B[0;32m      5\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m en, fr, p \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(engs, fras, preds):\n\u001B[0;32m      6\u001B[0m     translation \u001B[38;5;241m=\u001B[39m []\n",
      "Cell \u001B[1;32mIn[20], line 4\u001B[0m\n\u001B[0;32m      1\u001B[0m engs \u001B[38;5;241m=\u001B[39m [\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgo .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mi lost .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhe\u001B[39m\u001B[38;5;130;01m\\'\u001B[39;00m\u001B[38;5;124ms calm .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mi\u001B[39m\u001B[38;5;130;01m\\'\u001B[39;00m\u001B[38;5;124mm home .\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m      2\u001B[0m fras \u001B[38;5;241m=\u001B[39m [\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mva !\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mj\u001B[39m\u001B[38;5;130;01m\\'\u001B[39;00m\u001B[38;5;124mai perdu .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mil est calme .\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mje suis chez moi .\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m      3\u001B[0m preds, _ \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mpredict_step(\n\u001B[1;32m----> 4\u001B[0m     \u001B[43mdata\u001B[49m\u001B[38;5;241m.\u001B[39mbuild(engs, fras), d2l\u001B[38;5;241m.\u001B[39mtry_gpu(), data\u001B[38;5;241m.\u001B[39mnum_steps)\n\u001B[0;32m      5\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m en, fr, p \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(engs, fras, preds):\n\u001B[0;32m      6\u001B[0m     translation \u001B[38;5;241m=\u001B[39m []\n",
      "File \u001B[1;32m_pydevd_bundle\\pydevd_cython_win32_310_64.pyx:1187\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_win32_310_64.SafeCallWrapper.__call__\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32m_pydevd_bundle\\pydevd_cython_win32_310_64.pyx:627\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_win32_310_64.PyDBFrame.trace_dispatch\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32m_pydevd_bundle\\pydevd_cython_win32_310_64.pyx:937\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_win32_310_64.PyDBFrame.trace_dispatch\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32m_pydevd_bundle\\pydevd_cython_win32_310_64.pyx:928\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_win32_310_64.PyDBFrame.trace_dispatch\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32m_pydevd_bundle\\pydevd_cython_win32_310_64.pyx:585\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_win32_310_64.PyDBFrame.do_wait_suspend\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mC:\\Program Files\\JetBrains\\DataSpell 2023.3.4\\plugins\\python-ce\\helpers\\pydev\\pydevd.py:1184\u001B[0m, in \u001B[0;36mPyDB.do_wait_suspend\u001B[1;34m(self, thread, frame, event, arg, send_suspend_message, is_unhandled_exception)\u001B[0m\n\u001B[0;32m   1181\u001B[0m         from_this_thread\u001B[38;5;241m.\u001B[39mappend(frame_id)\n\u001B[0;32m   1183\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_threads_suspended_single_notification\u001B[38;5;241m.\u001B[39mnotify_thread_suspended(thread_id, stop_reason):\n\u001B[1;32m-> 1184\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_do_wait_suspend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mthread\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mframe\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mevent\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43marg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msuspend_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfrom_this_thread\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mC:\\Program Files\\JetBrains\\DataSpell 2023.3.4\\plugins\\python-ce\\helpers\\pydev\\pydevd.py:1199\u001B[0m, in \u001B[0;36mPyDB._do_wait_suspend\u001B[1;34m(self, thread, frame, event, arg, suspend_type, from_this_thread)\u001B[0m\n\u001B[0;32m   1196\u001B[0m             \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call_mpl_hook()\n\u001B[0;32m   1198\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprocess_internal_commands()\n\u001B[1;32m-> 1199\u001B[0m         \u001B[43mtime\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msleep\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m0.01\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1201\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcancel_async_evaluation(get_current_thread_id(thread), \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mid\u001B[39m(frame)))\n\u001B[0;32m   1203\u001B[0m \u001B[38;5;66;03m# process any stepping instructions\u001B[39;00m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "engs = ['go .', 'i lost .', 'he\\'s calm .', 'i\\'m home .']\n",
    "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
    "preds, _ = model.predict_step(\n",
    "    data.build(engs, fras), d2l.try_gpu(), data.num_steps)\n",
    "for en, fr, p in zip(engs, fras, preds):\n",
    "    translation = []\n",
    "    for token in data.tgt_vocab.to_tokens(p):\n",
    "        if token == '<eos>':\n",
    "            break\n",
    "        translation.append(token)\n",
    "    print(f'{en} => {translation}, bleu,'\n",
    "          f'{d2l.bleu(\" \".join(translation), fr, k=2):.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-18T09:21:22.650384Z",
     "start_time": "2024-03-18T09:21:22.642383Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Available: True\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# 检查CUDA（GPU支持）是否可用\n",
    "cuda_available = torch.cuda.is_available()\n",
    "print(\"CUDA Available:\", cuda_available)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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